Method and apparatus for identifying vehicle information from an image

ABSTRACT

Some aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal. The optical image includes an image of a vehicle license plate. The mobile apparatus includes a license plate detector configured to process the electrical signal to recover information from the vehicle license plate image.

CLAIM OF BENEFIT TO PRIOR APPLICATIONS

The present application is a continuation-in-part application of U.S. patent application Ser. No. 15/616,823, filed on Jun. 7, 2017, and entitled “METHOD AND APPARATUS FOR BLOCKING INFORMATION FROM AN IMAGE”, which is a continuation-in-part application of U.S. patent application Ser. No. 14/613,323, filed on Feb. 3, 2015, and entitled “METHOD AND APPARATUS FOR RECOVERING LICENSE PLATE INFORMATION FROM AN IMAGE,” which is a continuation-in-part application of U.S. patent application Ser. No. 14/455,841, filed on Aug. 8, 2014, entitled “METHOD AND SYSTEM FOR PROVIDING A LISTING OF VEHICLES BASED ON VEHICLE LICENSE PLATE INFORMATION FROM A SIMILAR VEHICLE,” which is a continuation-in-part application of U.S. patent application Ser. No. 14/318,397, filed on Jun. 27, 2014, and entitled “METHOD AND SYSTEM FOR PROVIDING A VALUATION DERIVED FROM AN IMAGE,” the contents of each of which are hereby incorporated by reference herein in their entirety.

BACKGROUND Field

The present disclosure relates generally to a method and apparatus for detecting license plate information from an image of a license plate and more specifically, detecting license plate information from an optical image, captured by a mobile apparatus, that includes a license plate image and several other object images.

Background

In recent years, collecting still images of license plates has become a common tool used by authorities to catch the drivers of vehicles that may engage in improper or unlawful activity. For example, law enforcement authorities have set up stationary traffic cameras to photograph the license plates of vehicles that may be traveling above a posted speed limit at a specific portion of a road or vehicles that drive through red lights. Toll booth operators also commonly use such stationary cameras to photograph vehicles that may pass through a toll booth without paying the required toll. However, all of these scenarios have a common thread. The camera must be manually installed and configured such that it will always photograph the vehicle's license plate at a specific angle and when the vehicle is in a specific location. Any unexpected modifications, such as a shift in angle or location of the camera would render the camera incapable of properly collecting license plate images.

Additionally, camera equipped mobile apparatuses (e.g., smartphones) have become increasingly prevalent in today's society. Mobile apparatuses are frequently used to capture optical images and for many users serve as a replacement for a simple digital camera because the camera equipped mobile apparatus provides an image that is often as good as those produced by simple digital cameras and can easily be transmitted (shared) over a network.

The positioning constraints put on the traffic cameras make it difficult to take images of license plates from different angles and distances and still achieve an accurate reading. Therefore, it would be difficult to scale the same license plate image capture process performed by law enforcement authorities to mobile apparatuses. In other words, it is difficult to derive license plate information from an image of a license plate taken from a mobile image capture apparatus at a variety of angles, distances, ambient conditions, mobile apparatus motion, and when other object images are also in the image, which hinders a user's ability to easily gather valuable information about specific vehicles when engaging in a number of different vehicle related activities such as buying and selling vehicles, insuring vehicles, and obtaining financing for vehicles.

Another disadvantage of existing systems is the sometime need to block the license plate of one or more vehicles when creating an image of a vehicle to be used in a non-secure environment, such as a public network (e.g. Internet). If a user wishes to advertise a vehicle for sale or for other reasons, the license plate should be obscured to protect personal information about the vehicle and vehicle owner.

Another disadvantage of existing systems is an inefficiency in identifying the plates due to the presence of multiple license issuers and a difficulty in confirming identification of the plates without resorting to more slower and more cumbersome methods of identification.

SUMMARY

Several aspects of the present invention will be described more fully hereinafter with reference to various methods and apparatuses.

Some aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal. The optical image includes an image of a vehicle license plate. The mobile apparatus includes a license plate detector configured to process the electrical signal to recover information from the vehicle license plate image.

Other aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal. The optical image includes several object images. One of the object images includes a vehicle license plate image. The mobile apparatus includes a license plate detector configured to process the electrical signal to recover information from the vehicle license plate image from a portion of the electrical signal corresponding to said one of the object images.

Other aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal. The mobile apparatus includes a display. The mobile apparatus includes a rendering module configured to render the optical image to the display. The mobile apparatus includes an image filter configured to apply one or more filter parameters to the electrical signal based on at least one of color temperature of the image, ambient light, and motion of the apparatus. The mobile apparatus includes a license plate detector configured to process the electrical signal to recover information from the vehicle license plate image. The rendering module is further configured to overlay a detection indicator on the displayed image to assist the user position of the apparatus with respect to the optical image in response to a signal from the image filter. The rendering module is further configured to provide an alert to the display when the license plate detector fails to recover the vehicle license plate information.

Other aspects of the invention relate to a computer program product for a mobile apparatus having an image sensor configured to convert an optical image into an electrical signal. The optical image includes several object images. One of the object images includes an image of a vehicle license plate. The computer program product includes a machine readable medium including code to process the electrical signal to select said one of the object images. The machine readable medium includes code to process a portion of the electrical signal corresponding to the selected said one of the object images to recover information from the vehicle license plate image.

Other aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal. The optical image includes an image of a vehicle license plate. The mobile apparatus includes a timing circuit configured to sample the electrical signal at a frame rate. The mobile apparatus includes a license plate detector configured to process the sampled electrical signal to recover information from the vehicle license plate image.

Other aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal and to automatically obscure or block the license plate information and other identifying information to prevent the unwanted dissemination of the information.

Other aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal and to automatically replace the license plate information and other identifying information with a selected image to provide information or promotion of the system used to record information about the vehicle.

Other aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal and to utilize ancillary image data to provide additional metrics for confirming the plate number, issuing authority, and owner of the vehicle.

Other aspects of the invention relate to a mobile apparatus including an image sensor configured to identify a vehicle make, model, and or year from physical characteristics of the vehicle.

Other aspects of the invention relate to a mobile apparatus including an image sensor configured to determine if a vehicle identification made by a plate detector matches a vehicle identification made by physical characteristics.

It is understood that other aspects of methods and apparatuses will become readily apparent to those skilled in the art from the following detailed description, wherein various aspects of apparatuses and methods are shown and described by way of illustration. As understood by one of ordinary skill in the art, these aspects may be implemented in other and different forms and its several details are capable of modification in various other respects. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of processes and apparatuses will now be presented in the detailed description by way of example, and not by way of limitation, with reference to the accompanying drawings, wherein:

FIG. 1 conceptually illustrates an exemplary embodiment of an apparatus that is capable of capturing an optical image and detecting a license plate image from the optical image.

FIG. 2 illustrates an exemplary embodiment transmitting license plate information derived from an optical image to an external server.

FIG. 3 illustrates an exemplary embodiment of an apparatus for displaying a confirmation dialog.

FIG. 4 conceptually illustrates an exemplary embodiment of a process of obtaining license plate information from an optical image.

FIGS. 5a and 5b conceptually illustrate an exemplary embodiment of a process of obtaining license plate information from a video.

FIG. 6 illustrates an exemplary embodiment of a system architecture of a license plate detection apparatus.

FIG. 7 illustrates an exemplary embodiment of a diagram of the format converter.

FIG. 8 illustrates an exemplary embodiment of a diagram of the image filter.

FIG. 9 illustrates an exemplary embodiment of a diagram of a license plate detector.

FIG. 10 illustrates an exemplary embodiment of an object image with a convex hull fit around the object image.

FIG. 11 illustrates an exemplary embodiment of a method for forming a quadrilateral from a convex hull.

FIG. 12 illustrates an exemplary embodiment of an object image enclosed in a quadrilateral.

FIG. 13 illustrates an exemplary embodiment of a diagram of the rendering module.

FIG. 14 illustrates an exemplary embodiment of a scene that may be captured by a license plate detection apparatus.

FIG. 15 provides a high level illustration of an exemplary embodiment of how an image may be rendered on a mobile apparatus by the license plate detection apparatus and transmission of a detected license plate image to a server.

FIG. 16 conceptually illustrates an exemplary embodiment of a more detailed process for processing an electrical signal to recover license plate information.

FIG. 17 illustrates an exemplary embodiment of an object image comprising a license plate image within a rectangle.

FIG. 18 illustrates an exemplary embodiment of an object image comprising a license plate image within a quadrilateral.

FIG. 19 is an illustration of an exemplary embodiment of the dewarping process being performed on a license plate image.

FIG. 20 conceptually illustrates an exemplary embodiment of a process for processing an optical image comprising a license plate image.

FIG. 21 illustrates an exemplary embodiment of a diagram for determining whether a patch is an actual license plate image.

FIG. 22 conceptually illustrates an exemplary embodiment of a process for processing a patch comprising a candidate license plate image.

FIG. 23 illustrates an exemplary embodiment of an electronic system that may implement the license plate detection apparatus.

FIG. 24A illustrates an exemplary embodiment of a captured license plate image.

FIG. 24B illustrates an exemplary embodiment of a captured license plate image with the plate information obscured.

FIG. 24C illustrates an exemplary embodiment of a captured license plate image with the plate information replaced with a promotional image.

FIG. 25 illustrates an exemplary embodiment of a process of obtaining license plate information from an optical image and adding replacement or obscuring of the plate image.

FIGS. 26A and 26B conceptually illustrate an exemplary embodiment of a process of obtaining license plate information from a video and adding replacement or obscuring of the plate image.

FIG. 27 illustrates an exemplary embodiment of a diagram of the rendering module including a plate block/replacement module.

FIG. 28 illustrates an exemplary embodiment of a process of operation of the plate block/replacement module.

FIG. 29 is a flow diagram illustrating the operation of the system according to an exemplary embodiment.

FIG. 30 is a flow diagram illustrating the operation of the redundant data embodiment using the threshold system.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appended drawings is intended as a description of various exemplary embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form in order to avoid obscuring the concepts of the present invention. Acronyms and other descriptive terminology may be used merely for convenience and clarity and are not intended to limit the scope of the invention.

The word “exemplary” or “embodiment” is used herein to mean serving as an example, instance, or illustration. Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments. Likewise, the term “embodiment” of an apparatus, method or article of manufacture does not require that all embodiments of the invention include the described components, structure, features, functionality, processes, advantages, benefits, or modes of operation.

It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The term “and/or” includes any and all combinations of one or more of the associated listed items.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by a person having ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

In the following detailed description, various aspects of the present invention will be presented in the context of apparatuses and methods for recovering vehicle license plate information from an image. However, as those skilled in the art will appreciate, these aspects may be extended to recovering other information from an image. Accordingly, any reference to an apparatus or method for recovering vehicle license plate information is intended only to illustrate the various aspects of the present invention, with the understanding that such aspects may have a wide range of applications.

FIG. 1 conceptually illustrates an exemplary embodiment of an apparatus 130 that is capable of capturing an optical image and detecting a license plate 120 from the optical image. The apparatus 130 may be a mobile phone, personal digital assistants (PDA), smart phone, laptop computer, palm-sized computer, tablet computer, game console, media player, digital camera, or any other suitable apparatus. FIG. 1 includes a vehicle 110, the license plate 120 registered to the vehicle 110, the apparatus 130, touch screen 140, and a user 150. The apparatus 130, of some embodiments, may be a wireless handheld device with built in image capture capabilities such as the smart phone, tablet or personal data assistant (PDA) described above. However, in some aspects of the service, the apparatus 130 may be a digital camera capable of processing or transferring data derived from the captured image to a personal computer. The information may then be uploaded from the personal computer to the license plate detection apparatus discussed in the foregoing.

In an exemplary embodiment of the apparatus, a customized application is installed on the apparatus 130. The customized application may interface with the apparatus' image capture device to capture an optical image, convert the optical image to an electrical signal, process the electrical signal to detect the presence of a license plate image, and derive license plate information from a portion of the electrical signal that is associated with the license plate image. The license plate information may be transmitted wirelessly to a server for further processing or decoding such as optical character recognition (OCR) of the license plate image. Alternatively, the OCR process may be carried out on the mobile apparatus 130.

As shown in FIG. 1, the apparatus 130 may receive an interaction from the user 150 to capture an optical image that includes an object image of the license plate 120. The interaction may occur at the touch screen 140. The touch screen 140 shows an exemplary rendering of the optical image, including a rendering of the license plate image that may be captured by the apparatus 130. As illustrated on the touch screen 140, the image of the license plate 120 may include a number and a state. OCR software may be used to convert the state and number portions of the license plate image to text, which may be stored as strings to be used later for various functions. Once, a suitable image including a license plate image is captured by the apparatus 130, the license plate data may be recovered and transmitted to a server for further processing. Additionally, the server and/or the apparatus application may provide error checking capability to ensure that the captured image is clear enough to accurately detect and decode a license plate image. When the server or apparatus determines that a suitable image has not been captured, the apparatus 130 may display an alert in the display area 140, which may guide the user to acquiring a suitable image.

Alternatively, some aspects of the apparatus may provide the capability to bypass the image capture process to instead provide a user interface with text fields. For example, the user interface may provide text fields that allow for entry of the license plate number and state. The entered information may be provided as text strings to the license plate detection apparatus without going through the detection process discussed above.

FIG. 2 illustrates an exemplary embodiment of transmitting license plate information derived from an optical image to an external server 230. In some aspects of the apparatus, a license plate image may be transmitted after recovering the license plate information from an image 220. As shown, FIG. 2 includes an apparatus 210, the image 220, the server 230, and the Internet 240. The apparatus 210 may be a mobile or wireless apparatus. The image 220 may be the same as the image rendered on the display area 140 as illustrated in FIG. 1.

A license plate image recovered from the image 220 may be transmitted over the internet 240 to the server 230 where it is processed for the purpose of detecting whether the license plate image is suitable for deriving license plate data and/or for performing OCR on the license plate image to derive license plate information such as the state of origin and the license plate number.

Once the license plate image (or image file) is transmitted to the server 230, the apparatus 210 may receive and display a confirmation message for confirming that the derived license plate information (e.g., state and license plate number) is correct. In some aspects of the apparatus, the apparatus 210 may also display information about the vehicle to help the user determine whether the derived license plate information is correct. This may be useful in cases such as when the apparatus 210 captures a license plate image of a moving vehicle. The vehicle license plate may no longer be in eyesight. However, it may be possible to determine with some degree of accuracy whether the derived license plate information is correct based on the vehicle information that is displayed on the mobile apparatus.

FIG. 3 illustrates an exemplary embodiment of an apparatus 300 for displaying a confirmation dialog as discussed above. The apparatus 300 includes a display area 310. The display area 310 includes license plate information 320 and selectable user interface (UI) objects 330 and 335. As described above, the display area 310 may optionally also display information about the vehicle to which the displayed license plate information 320 is associated. For instance, the display area 310 may display a year, make, model, and/or trim of the vehicle to further help the user identify that the correct vehicle license plate information was derived. When the license plate information is not correct, the apparatus 300 may receive a selection of the UI object 335 from a user. Selection of any UI object may be performed by a gestural interaction with a touch screen on the apparatus 300. Such gestural interactions may include tapping and/or dragging motions. When the license plate information is deeming incorrect, the apparatus 300 may receive a selection of the UI object 330 from a user. The selections received at UI objects 330 and 335 may assist the apparatus in collecting information on how to better derive information from the license plate image. For instance, the server may collect statistics on how frequently a particular OCR software application provides correct and/or incorrect information. This may assist the apparatus in providing more reliable license plate information as time goes on.

The interface described in FIGS. 1-3 provides an easy and efficient way for buyers, sellers, and owners of vehicles as well as any other user to gather information about a vehicle based on the license plate information that is derived. The license plate information may be used for any number of functions such as searching for similar cars that are for sale, financing the purchase of a vehicle and/or obtaining insurance for a vehicle.

FIG. 4 conceptually illustrates an exemplary embodiment of a process 400 of obtaining license plate information from an optical image. The process 400 may be performed by a mobile apparatus such as the apparatus 130 described with respect to FIG. 1. The process 400 may begin after an image capture capability or an application is initiated on the mobile apparatus. In some aspects of the process, the application may enable the image capture feature on the mobile apparatus.

As shown, the process 400 captures (at 410) an optical image that includes a vehicle license plate image. As will be discussing in the following figure, some aspects of the apparatus may process a video. A frame may then be extracted and converted to an image file.

At 420, the process 400 converts the optical image into an electrical signal. The process 400 then processes (at 430) the electrical signal to recover license plate information. The process 400 determines (at 440) whether the license plate information was successfully recovered. When the license plate information was successfully recovered, the process ends. When the license plate information was not successfully recovered, the process 400 displays (at 450) an alert that the license plate information was not recovered. In some aspects of the process, a message guiding the user to position the mobile apparatus to achieve greater chances of recovering the license plate information may be provided with the displayed alert. The process then ends. However, in some aspects of the process, rather than end, the process may optionally return to capture (at 410) another optical image and repeat the entire process 400.

FIGS. 5a and 5b conceptually illustrate an exemplary embodiment of a process 500 of obtaining license plate information from a video. The process 500 may be performed by a mobile apparatus such as the apparatus 130 described with respect to FIG. 1. The process 500 may begin after an image and/or video capture capability or an application is initiated on the mobile apparatus. The application may enable the image and/or video capture feature on the mobile apparatus.

As shown, the process 500 converts (at 505) an optical image into an electrical signal for sampling the electrical signal at n frames/second (fps). In some aspects of the process, the process may sample the electrical signal at intervals such as 24 fps or any other suitable interval for capturing video according to the apparatus' capabilities. Each sample of the electrical signal represents a frame of a video image presented on a display. The process 500 samples (at 510) a first portion of the electrical signal representing a first frame of the video image presented on the display. The process then determines (at 515) whether any object image(s) are detected within the frame. At least one of the detected object image(s) may comprise a license plate image. When the process 500 determines that at least object image exists within the frame, the process 500 assigns (at 520) a score based on the detected object image. The score may be based on the likelihood that at least one of the object images is a license plate image and is discussed in greater detail below with respect to FIGS. 21 and 22. The score may be applied to each object image and/or aggregated for each object image detected in the frame. The process may then store (at 525) the score and associated object image and frame information in a data structure. In some aspects of the process, the process 500 may store the aggregated object image score and/or the process 500 may store the highest scoring object image in the frame.

When the process 500 determines (at 515) that no object image exists within the frame or after the process 500 stores the score (at 525), the process 500 displays feedback to a user based on the object image detected (or not detected). For instance, when no object image is detected in the frame, the process 500 may display a message guiding the user on how to collect a better optical image. However, when at least one object image is detected in the frame, the process 500 may provide feedback by overlaying rectangles around the detected object image(s). Alternatively or conjunctively, the process 500 may overlay a rectangle that provides a visual cue such as a distinct color, indicating which object image is determined to most likely be a license plate image or has a higher score than other object images within the frame. In some aspects, the visual cue may be provided when a particular object image receives a score above a threshold value.

The process 500 optionally determines (at 535) whether user input has been received to stop the video. Such user input may include a gestural interaction with the mobile apparatus, which deactivates the camera shutter on the mobile apparatus. When the process 500 determines (at 535) that user input to stop the video capture is received, the process 500 selects (at 545) the highest scoring frame according to the stored frame information. When the process 500 determines (at 535) that user input to stop the video capture has not been received, the process 500 determines (at 540) whether to sample additional portions of the electrical signal. In some aspects of the process, such a determination may be based on a predetermined number of samples. For instance, the mobile apparatus may have a built in and/or configurable setting for the number of samples to process before a best frame is selected. In other aspects of the process, such a determination may be based on achieving a score for a frame or object image in a frame that is above a predetermined threshold value. In such aspects, the frame or frame comprising the object image that is above the threshold score will be selected (at 545). When process 500 determines that there are more portions of the electrical signal to be sampled, the process 500 samples (at 550) the next portion of the electrical signal representing the next frame of the video image presented on the display. The process 500 then returns to detect (at 515) object image(s) within the next frame. In some aspects of the process, the process may receive user input to stop the video capture at any point while process 500 is running. Specifically, the process is not confined to receiving user input to halt video capture after the feedback is displayed (at 530); the user input may be received at any time while the process 500 is running. In such aspects, if at least one object image has been scored, then the process 500 will still select (at 545) the highest scoring object image. However, if no object images were scored, then the process will simply end.

In some aspects of the process, the process 500 may optionally use the object image(s) detected in the previous sample to estimate the locations of the object images in the sample. Using this approach optimizes processing time when the process can determine that the mobile apparatus is relatively stable. For instance, the mobile apparatus may concurrently store gyro accelerometer data. The process 500 may then use gyro accelerometer data retrieved from the mobile apparatus to determine whether the mobile apparatus has remained stable and there is a greater likelihood that the object image(s) will be in similar locations. Thus, when the process 500 can determine that the mobile apparatus is relatively stable, the processing time for license plate detection may be decreased because less of the portion of the electrical signal that represents the video image would need to be searched for the license plate image.

Alternatively or conjunctively, the process 500 may not use information about object image(s) from the previous frame as a predictor. Instead, the process 500 may undergo the same detection and scoring process discussed above. Then, for each object image that overlaps an object image detected in a previous frame (e.g., the object images share similar pixels either by space and/or location in the frames), the previous frame receives a higher score. Information about the overlapping object image(s) may be maintained for optimized processing later on. Additionally, in some aspects of the apparatus, the license plate detection apparatus may maintain a table of matching object image(s) for the sampled portions of the electrical signal representing frames of video images over time. In such aspects, some object image(s) may exist in one or a few of the frames or some may exist in many or all frames and accordingly with higher scores. In such instances, all of the overlapping object images may be processed as discussed in greater detail in the foregoing sections and provided to the server for OCR or identification. This would lead to greater accuracy in actual license plate detection and OCR results.

Returning now to FIGS. 5a and 5 b, after selecting (at 545) the highest scoring frame, the process 500 processes (at 555) the electrical signal based on the information associated with the selected frame to recover license plate information. The process 500 then determines (at 560) whether license plate information was recovered from the electrical signal. When the process 500 determines (at 560) that license plate information has been recovered, the process ends. When the process 500 determines (at 560) that license plate information was not detected, the process 500 displays (at 565) an alert that license plate information cannot be recovered. Such an alert may guide the user to acquiring better video that is more likely to produce a readable license plate image. For instance, the alert may guide the user's mobile device position or angle. The process 500 may then return to collect additional video.

FIG. 6 illustrates an exemplary embodiment of a system architecture of a license plate detection apparatus. The plate detection apparatus may be a mobile apparatus such as the apparatus 130 described with respect to FIG. 1 or any other suitable mobile apparatus that has image capture and processing capabilities.

The license plate detection apparatus includes an image capture apparatus 605, an imager 610, a keypad 615, a strobe circuit 685, a frame buffer 690, a format converter 620, an image filter 625, a license plate detector 630, a network 635, a network interface 640, a gateway 645, a rendering module 650, and a display 655. The license plate detection apparatus may communicate with a server having OCR Module 660, and an OCR analytics storage 670. However, in some aspects of the apparatus, the OCR module and/or OCR analytics storage may be part of the mobile apparatus. The license plate detection apparatus illustrated in FIG. 6 ultimately generates license plate information 675.

As shown, the image capture apparatus 605 communicates an optical image to the imager 610. The image capture apparatus 605 may comprise a camera lens and/or a camera that is built into a mobile apparatus. The imager 610 may comprise a CMOS array, NMOS, CCD, or any other suitable image sensor that converts an optical image into an electrical signal (e.g., raw image data). The electrical signal comprises pixel data associated with the captured image. The amount of pixel data is dependent on the resolution of the captured image. The pixel data is stored as numerical values associated with each pixel and the numerical values indicate characteristics of the pixel such as color and brightness. Thus, the electrical signal comprises a stream of raw data describing the exact details of each pixel derived from the optical image. During the image capture process, the imager 610 may produce a digital view as seen through the image capture apparatus for rendering at the display 655.

In some aspects of the apparatus, the image capture apparatus 605 may be configured to capture video. In such aspects, a timing circuit, such as the strobe circuit 685, may communicate with the imager 610. The strobe circuit 685 may sample (or clock) the imager 610 to produce a sampled electrical signal at some periodicity such as 24-30 fps. The sampled electrical signal may be representative of a frame of video presented on the display 655. The electrical signal may be provided to the frame buffer 690. However, the imager 610 may communicate the electrical signal directly to the format converter 620 when a single optical image is captured. When video is captured, the frame buffer may communicate the sample of the electrical signal representative of the frame of video from the frame buffer to the format converter 620. However, in some aspects of the apparatus, the frame buffer 690 may be bypassed such that the sampled electrical signal is communicated directly to the format converter 620.

The format converter 620 generates or compresses the raw image pixel data provided in the electrical signal to a standard, space efficient image format. However, in some aspects of the apparatus, the frame buffer 690 and format converter 620 may be reversed such that the sampled electrical signals are converted to a compressed standard video format before buffering. The standard image and/or video format can be read by the following modules of the license plate detection apparatus. However, the following description will assume that the sampled electrical signals are buffered before any such format conversion. The format converter 620 will be described in greater detail in FIG. 7.

The format converter 620 communicates the standard image file (or image) to the image filter 625. The image filter 625 performs a variety of operations on the image to provide the optimal conditions to detect a license plate image within the image. Such operations will be described in greater detail in FIG. 8. However, if the image filter 625 determines that the image is too distorted, noisy, or otherwise in a condition that is unreadable to the point that any filtering of the image will not result in a viable image for plate detection, the image filter 625 will signal to the rendering module 650 to display an alert on the display 655 that the image is not readable. Alternatively, once the image is filtered, ideally the image should be in a state that is optimal for accurate license plate detection. Therefore, the image filter 625 will then communicate the filtered image to the license plate detector 630.

The plate detector 630 is an integral module of license plate detection apparatus. The plate detector 630 will process the image to detect the presence of a license plate image by implementing several processes which will be described in greater detail in FIG. 9. The license plate detector may generate overlays such as rectangular boxes around object images it detects as potential or candidate license plate images. The overlays may be transmitted as signals from the license plate detector 630 to the rendering module 650. The rendering module may instruct the display 655 to display the overlays over the image received from the imager 610 so that the user of the mobile apparatus can receive visual guidance relating to what object images the license plate detection apparatus detects as candidate license plate images. Such information is useful in guiding the user to capture optical images that include the license plate image and provide a higher likelihood of accurate license plate information recovery.

The license plate detector 630 will determine which portion of the image (or electrical signal) is most likely a license plate image. The license plate detector 630 will then transmit only the license plate image portion of the image to the network 635. Alternatively, a user may skip the entire image conversion process and using the keypad 615, key in the license plate information, which is then transmitted over the network 635. The network 635 then transmits the license plate image information (or image file) or keyed information to the network interface 640, which transmits signals to the gateway 645.

The gateway 645 may transmit the license plate image data to the OCR module 660. The OCR module 660 derives the license plate information such as state and number information from the license plate image. The OCR module 660 may use several different third party and/or proprietary OCR applications to derive the license plate information. The OCR module 660 may use information retrieved from the OCR analytics storage 670 to determine which OCR application has the greatest likelihood of accuracy in the event that different OCR applications detected different characters. For instance, the OCR analytics storage 670 may maintain statistics collected from the user input received at the apparatus 300 described with respect to FIG. 3. The OCR module 660 may then select the license plate information that is statistically most likely to be accurate using information retrieved from the analytics storage 670. The OCR module 660 may then transmit the license plate information 675 as a text string or strings to the gateway 645, which provides the license plate information 675 to the rendering module 650 through the network interface 640. The rendering module 650 may then instruct the display 655 to display the license plate information 675. The display 655 may then display a message similar to the one described with respect to FIG. 3.

In the event that the OCR module 660 or the license plate detector 630 is unable to detect a license plate image or identify any license plate information, the OCR module 660 and/or the license plate detector 630 will signal an alert to the rendering module 650, which will be rendered on the display 655.

In some aspects of the apparatus, the OCR module 660 may be located on an apparatus separate from an external server. For instance, the OCR module 660 may be located on the mobile apparatus 130 similar to the license plate detection apparatus. Additionally, in some aspects of the apparatus, the format converter 620, image filter 625, and license plate detector 630 may be located on an external server and the electrical signal recovered from the optical image may be transmitted directly to the network 630 for processing by the modules on the external server.

The license plate detection apparatus provide several advantages in that it is not confined to still images. As discussed above, buffered or unbuffered video may be used by the license plate detection apparatus to determine the frame with the highest likelihood of having a license plate image. It also enables optical images to be taken while a mobile apparatus is moving and accounts for object images recovered from any angle and/or distance. Additionally, the license plate detection apparatus also provides the added benefit of alerting the user when a license plate image cannot be accurately detected in addition to guidance relating to how to get a better image that is more likely to produce license plate information. Such guidance may include directional guidance such as adjusting the viewing angle or distance as well as guidance to adjust lighting conditions, if possible. Thus, the license plate detection apparatus provides a solution to the complicated problem of how to derive license plate information captured from moving object images and from virtually any viewing angle.

FIG. 7 illustrates an exemplary embodiment of a diagram of the format converter 620. The format converter 620 receives the input of an electrical signal that defines an image 720 or an electrical signal that defines a sequence of sampled images such as video frames 725. The format converter 620 outputs an image file 730 in a standard format such as the formats discussed above with respect to FIG. 1. The format converter 620 includes a frame analyzer 715 and a conversion engine 710. When an electrical signal defining an image 720 is received at the format converter 620, the electrical signal will be read by the conversion engine 710. The conversion engine 710 translates the pixel data from the electrical signal into a standard, compressed image format file 730. Such standard formats may include .jpeg, .png, .gif, .tiff or any other suitable image format similar to those discussed with respect to FIG. 1. In the exemplary instance where the format converter 620 converts video to a standard format, the standard format may include .mpeg, .mp4, .avi, or any other suitable standard video format. Since the electrical signal received at the format converter 620 is raw data which can make for a very large file, the conversion engine may compress the raw data into a format that requires less space and is more efficient for information recovery.

The format converter 620 may also receive a several sampled electrical signals, each representing frames of video images, such as frame data 725. The video data frames may be received at the frame analyzer 715 in the format converter 620. The frame analyzer 715 may perform a number of different functions. For instance, the frame analyzer 715 may perform a function of analyzing each frame and discarding any frames that are blurry, noisy, or generally bad candidates for license plate detection based on some detection process such as the process 500 described in FIG. 5. Those frames that are suitable candidates for license plate detection may be transmitted to the conversion engine 710 and converted to the standard format image 730 similar to how the image 720 was converted.

FIG. 8 illustrates an exemplary embodiment of a diagram of the image filter 625. The image filter 625 receives a formatted image file that the image filter 625 is configured to read. The image filter 625 outputs a filtered image 840 which may be optimized for more reliable license plate recognition. Alternatively, if the image filter 625 determines that the image is unreadable, the image filter 625 may signal an alert 845, indicating to the user that the image is unreadable and/or guide the user to capture a better image.

The image filter 625 includes a filter processor 805, a grayscale filter 810, and a parameters storage 835. When the image filter 625 receives the formatted image file 830, the filter processor 805 will retrieve parameters from the parameters storage 835, which will assist the filter processor 805 in how to optimally filter the image. For instance, if the received image was taken in cloudy conditions, the filter processor 805 may adjust the white balance of the image based on the parameters retrieved from the parameters storage 835. If the image was taken in the dark, the filter processor 805 may use a de-noise function based on the parameters retrieved from the parameters storage 835 to remove excess noise from the image. In some aspects of the apparatus, the filter processor 805 also has the ability to learn based on the success of previously derived license plate images what parameters work best or better in different conditions such as those conditions described above. In such aspects, the filter processor 805 may take the learned data and update the parameters in the parameters storage 835 for future use.

The filter processor 805 also has logic to determine if an image will be readable by the license plate detector 630. When the filter processor 805 determines that the image will not be readable by the license plate detector 630, the filter processor 805 may signal an alert 845 to the rendering module 650. However, when the filter processor 805 determines that sufficient filtering will generate a readable image for reliable license plate detection, the filter processor 805 communicates the image, post filtering, to the grayscale filter 810.

Additionally, in some aspects of the apparatus, the image filter 625 may receive several images in rapid succession. Such instances may be frames of a video that may be captured while a mobile apparatus is moving. In such instances, the filter processor 805 may continuously adjust the filter parameters to account for each video frame, it receives. The same alerts may be signaled in real-time in the event that a video frame is deemed unreadable by the filter processor 805.

The grayscale filter 810 will convert the received image file to grayscale. More specifically, the grayscale filter will convert the pixel values for each pixel in the received image file 830 to new values that correspond to appropriate grayscale levels. In some aspects of the filter, the pixel values may be between 0 and 255 (e.g., 256 values or 2⁸ values). In other aspects of the filter, the pixel values may be between 0 and any other value that is a power of 2 minus 1, such as 1023, etc. The image is converted to grayscale, to simplify and/or speed up the license plate detection process. For instance, by reducing the number of colors in the image, which could be in the millions, to shades of gray, the license plate image search time may be reduced.

In the grayscale image, regions with higher intensity values (e.g., brighter regions) of the image will appear brighter than regions of the image with lower intensity values. The grayscale filter 810 ultimately produces the filtered image 840. However, one skilled in the art should recognize that the ordering of the modules is not confined to the order illustrated in FIG. 8. Rather, the image filter may first convert the image to grayscale using the grayscale filter 810 and then filter the grayscale image at the filter processor 805. The filter processor 805 then outputs the filtered image 840. Additionally, it should be noted that the image filter 625 and the format converter 620 may be interchangeable. Specifically, the order in which this image is processed by these two modules may be swapped in some aspects of the apparatus.

FIG. 9 illustrates an exemplary embodiment of a diagram of the license plate detector 630. The license plate detector 630 receives a filtered image 930 and processes the image to determine license plate information 935, which is may be a cropped image of at least one license plate image. The license plate detector 630 comprises an object detector 905, a quad processor 910, a quad filter 915, a region(s) of interest detector 920, and a patch processor 925. The license plate detector 630 provides the integral function of detecting a license plate image from an image at virtually any viewing angle and under a multitude of conditions, and converting it to an image that can be accurately read by at least one OCR application.

The license plate detector 630 receives the filtered image 930 at the object detector 905. As discussed above, the filtered image 930 has been converted to a grayscale image. The object detector 905 may use a mathematical method, such as a Maximal Stable Extremal Regions (MSER) method, for detecting regions in a digital image that differ in properties, such as brightness or color, compared to areas surrounding those regions. Simply stated, the detected regions of the digital image have some properties that are constant or vary within a pre-described range of values; all the points (or pixels) in the region can be considered in some sense to be similar to each other. This method of object detection may provide greater accuracy in the license plate detection process than other processes such as edge and/or corner detection. However, in some instances, the object detector 905 may use edge and/or corner detection methods to detect object images in an image that could be candidate license plate images.

Typically, the object images detected by the object detector 905 will have a uniform intensity throughout each adjacent pixel. Those adjacent pixels with a different intensity would be considered background rather than part of the object image. In order to determine the object images and background regions of the filtered image 930, the object detector 905 will construct a process of applying several thresholds to the image. Grayscale images may have intensity values between 0 and 255, 0 being black and 255 being white. However, in some aspects of the apparatus, these values may be reversed with 0 being white and 255 being black. An initial threshold is set to be somewhere between 0 and 255. Variations in the object images are measured over a pre-determined range of threshold values. A delta parameter indicates through how many different gray levels a region needs to be stable to be considered a potential detected object image. The object images within the image that remain unchanged, or have little variation, over the applied delta thresholds are selected as likely candidate license plate images. In some aspects of the detector, small variations in the object image may be acceptable. The acceptable level of variations in an object image may be programmatically set for successful object image detection. Conversely or conjunctively, the number of pixels (or area of the image) that must be stable for object image detection may also be defined. For instance, a stable region that has less than a threshold number of pixels would not be selected as an object image, while a stable region with at least the threshold number of pixels would be selected as an object image. The number of pixels may be determined based on known values relating to the expected pixel size of a license plate image or any other suitable calculation such as a height to width ratio.

In addition, the object detector 905 may recognize certain pre-determined textures in an image as well as the presence of informative features that provide a greater likelihood that the detected object image may be a license plate image. Such textures may be recognized by using local binary patterns (LBP) cascade classifiers. LBP is especially useful in real-time image processing settings such as when images are being captured as a mobile apparatus moves around an area. Although commonly used in the art for image facial recognition, LBP cascade classifiers may be modified such that the method is optimized for the detection of candidate license plate images.

In an LBP cascade classification, positive samples of an object image are created and stored on the license plate detection apparatus. For instance, a sample of a license plate image may be used. In some instances multiple samples may be needed for more accurate object image detection considering that license plates may vary from state to state or country to country. The apparatus will then use the sample object images to train the object detector 905 to recognize license plate images based on the features and textures found in the sample object images. LBP cascade classifiers may be used in addition to the operations discussed above to provide improved detection of candidate license plate images.

Once the object detector 905 has detected at least one object as a candidate license plate image, the object detector 905 will pass information relating to the detected object images to the quad processor 910 and/or the quad filter 915. In some aspects of the detector, the object images may not be of a uniform shape such as a rectangle or oval. The quad processor 910 will then fit a rectangle around each detected object image based on the object image information provided by the object detector 905. Rectangles are ideal due to the rectangular nature of license plates. As will be described in the foregoing, information about the rectangles may be used to overlay rectangles on object images that are displayed for the user's view on a mobile apparatus.

The rectangle will be sized such that it fits minimally around each object image and all areas of the object image are within the rectangle without more additional background space than is necessary to fit the object image. However, due to various factors such as the angle at which the optical image was taken, the license plate image may not be perfectly rectangular. Therefore, the quad processor 910 will perform a process on each object image using the rectangle to form a quadrilateral from a convex hull formed around each object image.

The quad processor 910 will use an algorithm that fits a quadrilateral as closely as possible to the detected object images in the image. For instance, the quad processor 910 will form a convex hull around the object image. A convex hull is a polygon that fits around the detected object image as closely as possible. The convex hull comprises edges and vertices. The convex hull may have several vertices. The quad processor 910 will take the convex hull and break it down to exactly four vertices (or points) that fit closely to the object image.

FIGS. 10-12 illustrate the functionality of the quad processor 910. As shown, FIG. 10 illustrates an exemplary embodiment of an object image 1005 with a convex hull 1010 fit around the object image 1005, and a blown up region 1015. The convex hull 1010 comprises several edges and vertices including vertices A-D. In order to fit the object image 1005 into a quadrilateral, the convex hull 1010 may be modified such that only 4 vertices are used. For instance, as illustrated in FIG. 11, for each adjacent pair of points A-D in the convex hull 1010, the quad processor 910 will find a new point Z that maintains convexity and enclosure of the object image when B and C are removed. Point Z is chosen as a point that provides a minimal increase to the hull area and does not go outside of the originally drawn rectangle (not shown). Thus, FIG. 11 illustrates an exemplary embodiment of a method for forming a quadrilateral (shown in FIG. 12) from the convex hull 1010. The process repeats for each set of 4 points until the convex hull 1010 is compressed to only four vertices as illustrated in FIG. 12.

FIG. 12 illustrates an exemplary embodiment of the object image 1005 enclosed in a quadrilateral 1210. As shown in FIG. 12, Quadrilateral 1210 fits as closely to the object image 1005 as possible without any edge overlapping the object image 1005. Fitting a quadrilateral closely to an object image as illustrated by the FIGS. 10-12 provides the benefit of greater efficiency in the license plate detection process. As will be described below, the license plate detection apparatus will only search the portions of the image within the quadrilaterals for the presence of a license plate image.

Referring back to FIG. 9, now that the quad processor 910 has drawn efficient quadrilaterals around each of the detected object images, the coordinates of the quadrilaterals are passed to the quad filter 915 and/or the region(s) of interest detector 920. As discussed above, the license plate detection apparatus first overlays rectangles around each detected object image. The quad filter 915 may use the rectangle information (rather than the quadrilateral information) received from the object detector 905, such as the pixel coordinates of the rectangles in the image, and look for rectangles similar in size and that overlap. The quad filter 915 will then discard the smaller rectangle(s), while maintaining the biggest. If at least two rectangles are of an identical size and overlap, the quad filter 915 will use a mechanism to determine which rectangle is more likely to be a full license plate image and discard the less likely image within the other rectangle(s). Such mechanisms may involve textures and intensity values as determined by the object detector 905. In some aspects of the filter, rather than searching only rectangles, the quad filter 915 may alternatively or additionally search the quadrilateral generated by the quad processor 910 for duplicates and perform a similar discarding process. By filtering out the duplicates, only unique object images within the rectangles will remain, with the likelihood that at least one of those object images is a license plate image. Thus, at this point, the license plate detection apparatus will only need to search the areas within the rectangles or quadrilaterals for the license plate image.

The region(s) of interest detector 920 will then determine which of the object images are actually object images that that have similar proportions (e.g., height and width) to the proportions that would be expected for a license plate image. For instance, typically a license plate is rectangular in shape. However, depending on several factors such as the angle that the license plate image was captured, the object image may appear more like a parallelogram or trapezoid. However, there is a limit to how much skew or keystone (trapezoidal shape) a license plate image undergoes before it becomes unreadable. Therefore, it is necessary to compute a skew factor and/or keystone to determine whether the object image may be a readable license plate image. Specifically, object images that have a skew factor and/or keystone below and/or above a threshold value are likely object images that do not have the proportions expected for a license plate image or would likely be unreadable. Since a license plate has an expected proportion a threshold skew factor and/or keystone may be set and any detected object image that has a skew factor and/or keystone indicating that the object image is not a readable license plate image will be discarded. For instance, license plate images with a high skew and/or high keystone may be discarded.

In some aspects of the apparatus, the skew and keystone thresholds may be determined by digitally distorting known license plate images with varying amounts of pitch and yaw to see where the identification process and/or OCR fails. The threshold may also be dependent on the size of the object image or quadrilateral/trapezoid. Thus, quadrilaterals or trapezoids must cover enough pixel space to be identified and read by the OCR software. Those that do not have a large enough pixel space, skew factors that are too high, and/or keystones that are too high would then be discarded as either being unlikely candidates for license plate images or unreadable license plate images.

The skew factor is computed by finding the distance between opposing vertices of the quadrilateral and taking the ratio of the shorter distance to the longer distance so that the skew factor is less than or equal to 1. Rectangles and certain parallelograms that are likely candidate license plate images will have a skew factor that is close to 0, while skewed parallelograms will have a high skew factor. Additionally, trapezoids that are likely candidate license plate images will have a keystone that is close to 0, while trapezoids that are unlikely candidate license plate images will have a high keystone. Therefore, object images with a high skew factor are discarded, while the parallelograms with a lower skew factor and trapezoids with a lower keystone are maintained. In some aspects of the apparatus, a threshold skew and a threshold keystone may be defined. In such aspects, parallelograms having a skew factor below the threshold are maintained while those above the threshold are discarded. Similarly, in such aspects, trapezoids having a keystone below the threshold are maintained while those above the threshold are discarded. When the value is equal to the threshold, the parallelogram or trapezoid may be maintained or discarded depending on the design of the apparatus.

The remaining parallelograms and trapezoids are then dewarped. The dewarping process is particularly important for the trapezoids because it is used to convert the trapezoid into a rectangular image. The dewarping process uses the four vertices of the quadrilateral and the 4 vertices of an un-rotated rectangle with an aspect ratio of 2:1 (width:height), or any other suitable license plate aspect ratio, to compute a perspective transform. The aspect ratio may be pixel width:pixel height of the image. The perspective transform is applied on the region around the quadrilateral and the 2:1 aspect ratio object image is cropped out. The cropped object image, or patch, is an object image comprising a candidate license plate image.

The patch is then provided to the patch processor 925, which will search for alpha numeric characters in the patch, find new object images within the patch, fit rectangles around those object images, and compute a score from the fit rectangles. The score may be based on a virtual line that is drawn across the detected object images. If a line exists that has a minimal slope, the object images on that line may receive a score that indicates the object image is highly likely to be a license plate image. If no line with a minimal slope is detected, then an alert may be returned to the rendering module that a license plate image was not detected in the image. Scores may be calculated for several different patches from the same image and it follows that more than one license plate image may be detected in the same image. Once, the presence of a license plate image is detected, the license plate information 935 may be transmitted to a server for OCR and further processing. In some aspects of the apparatus, the license plate information is an image file comprising the license plate image. Additionally, the process for scoring the patch will be described in more detail with respect to FIG. 21.

FIG. 13 illustrates an exemplary embodiment of a diagram of the rendering module 650. The rendering module 650 may receive as input alert information from the image filter 1335, or information about detected object images from the license plate detector 1330. The rendering module will then communicate rendering instructions 1340 to the display 655. The rendering module 650 includes an overlay processor 1305, a detection failure engine 1310, and an image renderer 1315.

The overlay processor 1305 receives information about the detected object images 1330 from the license plate detector 630. As discussed above, such information may include coordinates of detected object images and rectangles determined to fit around those object images. The rectangle information is then provided to the detection failure engine 1310, which will determine that object images have been detected by the license plate detector 630. The detection failure engine 1310 may then forward the information about the rectangles to the image renderer 1315, which will provide rendering instructions 1340 to the display for how and where to display the rectangles around the image received from the imager 610. Such information my include pixel coordinates associated with the size and location of the rectangle and color information. For instance, if the license plate detector 630 determines that a detected object image is more likely to be an actual license plate image than the other detected object images, the rendering module 650 may instruct the display 655 to display the rectangle around the more likely object image in a way that is visually distinct from other rectangles. For instance, the rectangle around the object image more likely to be a license plate image may be displayed in a different color than the other rectangles in the display.

However, in some instances, the license plate detector 630 may not detect any object images. In such instances, the overlay processor will not forward any rectangle information to the detection failure engine 1310. The detection failure engine 1310 will then determine there has been an object image detection failure and signal an alert to the image renderer 1315. The image renderer 1315 will then communicate the display rendering instructions 1340 for the alert to the display 655. The license plate detection alerts have been described in greater detail above.

Additionally, the image filter 625 may provide information to the image renderer 1315 indicating an alert that the captured image cannot be processed for some reason such as darkness, noise, blur, or any other reason that may cause the image to be otherwise unreadable. The alert information from the image filter 625 is provided to the image renderer 1315, which then provides the rendering display instructions 1340 to the display 655 indicating how the alert will be displayed. The image filter alerts have been discussed in detail above.

The following FIGS. 14-22 provide exemplary illustrations and processes detailing the functionality of the license plate detection module 630. FIGS. 14-22 are devised to illustrate how the license plate detection apparatus goes from an optical image comprising many object images to detecting a license plate image among the object images.

FIG. 14 illustrates an exemplary embodiment of a scene 1400 that may be captured by a mobile apparatus 1410. The mobile apparatus 1410 may be similar to the mobile apparatus 130 described with respect to FIG. 1. The scene 1400 includes a structure 1425, a road 1430, a vehicle 1420, and a license plate 1405. The mobile apparatus 1410 includes a display area 1415.

As illustrated, the mobile apparatus 1410 has activated the image capture functionality of the mobile apparatus 1410. The image capture functionality may be an application that controls a camera lens and imager built into the apparatus 1410 that is capable of taking digital images. In some aspects of the apparatus, the image capture functionality may be activated by enabling an application which activates the license plate detection apparatus capabilities described in FIG. 6. In this example, the mobile apparatus 1410 may be capturing a still image, several images in burst mode, or video, in real-time for processing by the license plate detection apparatus. For instance, the vehicle 1420 may be moving while the image capture process occurs, and/or the mobile apparatus may not be in a stationary position. In such instances, the license plate detection apparatus may determine the best video frame taken from the video.

FIG. 15 provides a high level illustration of an exemplary embodiment of how an image may be rendered on an apparatus 1500 by the license plate detection apparatus and transmission of a detected license plate image to a server 1550. As shown, the apparatus 1500 includes a display area 1515 and an exploded view 1555 of the image that is rendered in display area 1515. The exploded view 1555 includes object images 1525, rectangles 1520 that surround the object images, overlapping rectangles 1530, candidate license plate image 1505, and a rectangle 1510 that surrounds the candidate license plate image 1505. In the event that a license plate image is detected and captured in the display area 1515, the apparatus 1500 may wirelessly transmit license plate image data 1535 over the Internet 1540 to a server 1550 for further processing. In some aspects of the apparatus, the license plate image data may be an image file comprising a license plate image.

As shown in exploded view 1555, the object detector 905 of the license plate detection apparatus has detected several object images 1525, as well as a candidate license plate image 1505. As shown, the rendering module 650 has used information communicated from the license plate detector 630 to overlay rectangles around detected object images 1525 including the candidate license plate image 1505. The rendering module 650 has also overlaid rectangles that differ in appearance around object images that are less likely to be license plate images. For instance, rectangles 1520 appear as dashed lines, while rectangle 1510 appears as a solid line. However, as those skilled in the art will appreciate, the visual appearance of the rectangles is not limited to only those illustrated in exploded view 1555. In fact, the visual appearance of the rectangles may differ by color, texture, thickness, or any other suitable way of indicating to a user that at least one rectangle is overlaid around an object image that is more likely to be a license plate image than the other object images in which rectangles are overlaid.

Exploded view 1555 also illustrates overlapping rectangles 1530. As discussed above, the quad filter 915 of the license plate detector 630 may recognize the overlapping rectangles 1530 and discard some of the rectangles, and detected object images within those discarded rectangles, as appropriate.

As is also illustrated by FIG. 15, the license plate detection apparatus has detected the presence of a candidate license plate image 1505 in the image. As a result, the mobile apparatus 1500 will transmit the license plate image data 1535 associated with the license plate image over the internet 1540 to the server 1550 for further processing. Such further processing may include OCR and using the license plate information derived from the OCR process to perform a number of different tasks that may be transmitted back to the mobile apparatus 1500 for rendering on the display area 1515. Alternatively, in some aspects of the apparatus, the OCR capability may be located on the mobile apparatus 1500 itself. In such aspects, the mobile apparatus may wirelessly transmit the license plate information such as state and number data rather than information about the license plate image itself.

FIG. 16 conceptually illustrates an exemplary embodiment of a more detailed process 1600 for processing an electrical signal to recover license plate information as discussed at a high level in process 400. The process may also be applied to detecting license plate information in a sample of an electrical signal representing a frame of a video image presented on a display as described in process 500 of FIG. 5. The process 1600 may be performed by the license plate detection apparatus. The process 1600 may begin after the mobile apparatus has activated an application, which enables the image capture feature of a mobile apparatus.

As shown, the process 1600 converts (at 1610) the captured image to grayscale. As discussed above, converting the image to grayscale makes for greater efficiency in distinguishing object images from background according to the level of contrast between adjacent pixels. Several filtering processes may also be performed on the image during the grayscale conversion process. The process 1600 then detects (at 1615) object image(s) from the grayscale image. Such object images may be the object images 1505 and 1525 as illustrated in FIG. 15. The process 1600 processes (at 1620) a first object image. The processing of object images will be described in greater detail in the foregoing description.

At 1625, the process 1600 determines whether an object image fits the criteria for a license plate image. When the object image fits the criteria for a license plate image, the process 1600 transmits (at 1630) the license plate image (or image data) to a server such as the server 1550. In some aspects of the process, an object image fits the criteria for a license plate when a score of the object image is above a threshold value. Such a score may be determined by a process which will be discussed in the foregoing description. The process 1600 then determines (at 1635) whether there are more object images detected in the image and/or whether the object image being processed does not exceed a threshold score.

When the process 1600 determines (at 1625) that an object image does not fit the criteria for a license plate image, the process 1600 does not transmit any data and determines (at 1635) whether more object images were detected in the image and/or whether the object image being processed did not exceed a threshold score. When the process 1600 determines that more object images were detected in the image and/or the object image being processed did not exceed a threshold score, the process 1600 processes (at 1640) the next object image. The process then returns to 1625 to determine if the object image fits the criteria of a license plate image.

When the process 1600 determines (at 1635) that no more object images were detected in the image and/or the object image being processed exceeds a threshold score, the process 1600 determines (at 1645) whether at least one license plate image was detected in the process 1600. When a license plate image was detected, the process ends. When a license plate image was not detected, an alert is generated (at 1650) and the rendering module 650 sends instructions to display a detection failure message at the display 655. In some aspects of the process, the detection failure alert may provide guidance to the user for capturing a better image. For instance, the alert may guide the user to move the mobile apparatus in a particular direction such as up or down and/or adjust the tilt of the mobile apparatus. Other alerts may guide the user to find a location with better lighting or any other suitable message that may assist the user such that the license plate detection apparatus has a greater likelihood of detecting at least one license plate image in an image.

The process 1600 may be performed in real-time. For instance, the process 1600 may be performed successively as more images are captured either by capturing several frames of video as the mobile apparatus or object images in the scene move and/or are tracked or by using an image capture device's burst mode. The process 1600 provides the advantage of being able to detect and read a license plate image in an image at virtually any viewing angle and under a variety of ambient conditions. Additionally, the criteria for determining a license plate image is determined based on the operations performed by the license plate detector. These operations will be further illustrated in the following figures as well.

FIGS. 17-19 illustrate the operations performed by the quad processor 910. For instance, FIG. 17 illustrates an exemplary embodiment of an image 1700 comprising a license plate image 1710. Although not shown, the license plate may be affixed to a vehicle which would also be part of the image. The image 1700 includes the license plate image 1710 and a rectangle 1705. As shown in exemplary image portion 1700, an object image has been detected by the object detector 905 of the license plate detector 630. The object image in this example is the license plate image 1710. After the object image was detected, the quad processor 910 fit a rectangle 1705 around the license plate image 1710. Information associated with the rectangle may be provided to the rendering module 650 for overlaying a rectangle around the detected license plate image in the image displayed on the display 655.

FIG. 18 illustrates an exemplary embodiment of a portion of an image 1800 comprising the same license plate image 1710 illustrated in FIG. 17. Image portion 1800 includes the license plate image 1810 and a quadrilateral 1805. As discussed above with respect to FIGS. 10-12, the quad processor 910 of the license plate detector 630 performs several functions to derive a quadrilateral that closely fits the detected object image. Once the quadrilateral has been derived, the quad processor 910 then computes the skew factor and/or keystone discussed above.

Once the quadrilateral is determined to have a low skew (or a skew below a threshold value) or the trapezoid has been determined to have a low keystone (or a keystone below a threshold value), the region(s) of interest detector 920 can dewarp the image to move one step closer to confirming the presence of a license plate image in the image and to also generate patch that is easily read by OCR software. In some aspects of the apparatus, the patch is the license plate image that has been cropped out of the image.

FIG. 19 is an exemplary embodiment of the dewarping process being performed on a license plate image 1905 to arrive at license plate image 1910. FIG. 19 illustrates two stages 1901 and 1902 of the dewarping process.

As shown, the first stage 1901 illustrates the license plate image 1905 in a trapezoidal shape similar to the shape of the quadrilateral 1805 illustrated in FIG. 18. The second stage 1902 illustrates the license plate image 1910 after the dewarping process has been performed. As shown, license plate image 1910 has undergone a perspective transform and rotation. The license plate image 1910 as shown in the second stage 1902 is now in a readable rectangular shape. In some aspects of the dewarping process, the license plate image may also undergo corrections if the license plate image is skewed or may scale the license plate image to a suitable size.

The ability to accurately dewarp quadrilaterals and especially the quadrilaterals that are license plate images taken at any angle is an integral piece of the license plate detection apparatus. The dewarping capability enables a user to capture an image of a license plate at a variety of different angles and distances. For instance, the image may be taken with any mobile apparatus at virtually any height, direction, and/or distance. Additionally, it provides the added benefit of being able to capture a moving image from any position. Once the license plate image has been dewarped, the region(s) of interest detector 920 will crop the rectangular license plate image to generate a patch. The patch will be used for further confirmation that the license plate image 1910 is, in fact, an image of a license plate.

FIG. 20 conceptually illustrates an exemplary embodiment of a process 2000 for processing an image comprising a license plate image. The process 2000 may be performed by a license plate detection apparatus. The process 2000 may start after the license plate detection apparatus has instantiated an application that enables the image capture feature of a mobile apparatus.

As shown, the process 2000 detects (at 2010) at least one object image, similar to the object image detection performed by process 1600. The following describes in greater detail the process of processing (at 1620) the image.

For instance, the process 2000 then fits (at 2015) a rectangle to each detected object image in order to reduce the search space to the detected object images. The information associated with the rectangle may also be used as an overlay to indicate to users of the license plate detection apparatus the location(s) of the detected object image(s). The process then uses the rectangles to form (at 2020) a convex hull around each object image. The convex hull, as discussed above, is a polygon of several vertices and edges that fits closely around an object image without having any edges that overlap the object image.

At 2025, the process 2000 compresses the convex hull to a quadrilateral that closely fits around the detected object image. The process of compressing the convex hull into a quadrilateral was discussed in detail with respect to FIGS. 9-12. The process 2000 then filters (at 2030) duplicate rectangles and/or quadrilaterals. In some aspects of the process, rectangles or quadrilaterals that are similar in size and overlap may be discarded based on some set criteria. For example, the smaller rectangle and/or quadrilateral may be discarded.

The process 2000 calculates (at 2035) a skew factor. The process 2000 then dewarps (at 2040) the quadrilateral. The process then crops (at 2045) the object image within the quadrilateral, which becomes the patch. The patch will be used for further processing as discussed below. In some aspects of the process, the object image is cropped at a particular ratio that is common for license plates of a particular region or type. For instance, the process may crop out a 2:1 aspect ratio patch, of the image, which is likely to contain the license plate image. Once the quadrilateral is cropped, the process 2000 then ends.

FIG. 21 illustrates an exemplary embodiment of a diagram that determines whether a patch 2100 is an actual license plate image. The patch 2100 includes a candidate license plate image 2105, alpha-numeric characters 2120 and 2140, rectangles 2115, sloped lines 2130, zero-slope line 2110, and graphic 2125.

As shown in the patch 2100, rectangles are fit around detected object images within the patch. In some aspects of the apparatus, object images may be detected using the MSER object detection method. Conjunctively or conversely, some aspects of the apparatus, may use edge and or corner detection methods to detect the object images. In this case, the detected object images are alpha-numeric characters 2120 and 2140 as well as graphic 2125. After detecting the alpha-numeric characters 2120 and 2140 as well as graphic 2125, a stroke width transform (SWT) may be performed to partition the detected object images into those that are likely from an alpha-numeric character and those that are not. For instance, the SWT may try to capture the only alpha-numeric effective features and use certain geometric signatures of alpha-numeric characters to filter out non-alpha-numeric areas, resulting in more reliable text regions. In such instances, the SWT transform may partition the alphanumeric characters 2120 and 2140 from the graphic 2125. Thus, only those object images that are determined to likely be alpha-numeric characters, such as alphanumeric characters 2120 and 2140, are later used in a scoring process to be discussed below. In some aspects of the apparatus, some object images other than alpha-numeric characters may pass through the SWT partitioning. Thus, further processing may be necessary to filter out the object images that are not alpha-numeric characters and also to determine whether the alpha-numeric characters in the license plate image fit the characteristics common for a license plate images.

Following the partitioning of alpha-numeric characters from non-alpha numeric characters, a line is fit to the center of the rectangle pair for each pair of rectangles. For instance, a sloped line is shown for the D and object image 2125 pair. The distance of all other rectangles to the lines 2130 and 2110 are accumulated and the pair with the smallest summed distance is used as a text baseline. For instance, the zero-slope line 2110 has the smallest summed distance of the rectangles to the line 2110. Some aspects of the apparatus may implement a scoring process to determine the presence of a license plate image. For instance, some aspects of the scoring process may determine a score for the determined alpha-numeric characters on the zero-slope line 2110. The score may increase when the rectangle around the alpha-numeric character is not rotated beyond a threshold amount. The score may decrease if the detected alpha-numeric character is too solid. In some aspects of the scoring process, solidity may be defined as the character area/rectangle area. When the calculated area is over a threshold amount, then the detected object image may be deemed too solid and the score decreases.

In other aspects of the scoring process, for each rectangle 2115 in the patch 2100 the patch score increases by some scoring value if the center of the rectangle is within a particular distance of the baseline line where X is the shorter of the rectangle height and width. For instance, if the particular distance were to be defined as the shorter of the rectangle height and width and if the scoring value is set at 1, the patch score value of the patch 2100 would be 7 because the rectangles around the characters “1DDQ976” are within a shorter distance than the width of the rectangle. Furthermore, the zero-slope of the line 2110 between the alpha-numeric characters 2120 further confirm that this patch is likely a license plate image since typically license plates have a string of characters along a same line. Sloped lines 2130 are, therefore, unlikely to provide any indication that the patch is a license plate image because the distance between characters is too great and the slope is indicative of a low likelihood of a license plate image. Accordingly, in some aspects of the process, sloped lines 2130 are discarded in the process.

In some aspects of the process, when the patch has a score above a threshold value, the patch is determined to be a license plate image, and the license plate detection is complete. The license plate image data is then transmitted to a server for further processing and for use in other functions computed by the server, the results of which are provided to the license plate detection apparatus.

FIG. 22 conceptually illustrates an exemplary embodiment of a process 2200 for processing a patch comprising a candidate license plate image such as patch 2100. The process may be performed by the license plate detection apparatus. The process may begin after a patch has been cropped from an image file.

As shown, the process 2200 processes (at 2205) only substantially rectangular portion(s) of the patch to locate alpha-numeric characters. The process 2200 fits (at 2210) rectangles around the located alpha-numeric characters and computes scores based on the distances between rectangle pairs as discussed above with respect to FIG. 21. The process 2200 selects (at 2215) the patch with the best score to recognize as a license plate image. Alternatively or conjunctively, the process 2200 may select all patches that have a score above a threshold level to be deemed as license plate images. In such instances, multiple patches, or instances of license plate information, would be transmitted to the server for further processing.

Automatic Plate Blocking/Replacement

In some embodiments, a user may wish to publish an image of a vehicle for various purposes. The user may wish to offer a vehicle for sale, to share an image of the vehicle, or to otherwise display an image of the vehicle. However, it is inadvisable to reveal the vehicle license plate information in an unsecure environment. There are a number of legal and illegal methods to determine the name and address of the vehicle owner, registration and fee history, and other information. This is in spite of the fact of the Drivers Privacy Protection Act (DPPA) that is intended to prevent the ability to obtain personal information using a vehicle license plate. In some cases, a site that posts images and videos of vehicles will not post the image if the license plate information is visible.

There are a number of methods to block the vehicle plate identification information in an image, either pre-image or post-image. Prior to taking the image of the vehicle, the user can manually cover or block the license plate with opaque or dark material, such as tape, cardboard, or the like. Sometimes a user will frame the image so that the license plate does not appear. In other instances, the user may hold an object in front of the camera (e.g. the photographer's finger, piece of paper, or the like) so that it covers the license plate during the taking of the picture.

In some cases, the image is modified after the image has been recorded, such as using a photo-editing tool to obscure the license plate digitally, or by manually obscuring the license plate on a print out of the image before further reproduction or distribution.

A problem with all of these approaches is when recording a moving video image of the vehicle, so that the license plate appears periodically in frames of the video and extra care must be taken to obscure the license plate before, during, and/or after the recording takes place.

FIG. 24A illustrates an example of an image of license plate image captured on a mobile device. The mobile device 210 is used to take an image of a vehicle. In some cases, the vehicle plate may be visible in the captured image or video. As shown in FIG. 24A the state and plate number are visible, creating potential privacy problems.

FIG. 24B illustrates the same image captured in one embodiment of the system with the image of the license plate automatically obscured during the image capture stage. In this embodiment, the system detects the license plate, and applies an obscuring overlay automatically over the license plate region so that no identifying information can be detected.

FIG. 24C illustrates the same image captured in one embodiment of the system where the license plate information is automatically replaced with a promotional image that both obscures the license plate information and provides promotion of a company. The promotion may be for the image capture system or any other suitable company or entity that has engaged

FIG. 25 is a flow diagram illustrating the operation of the plate blocking system in one embodiment. The process 2500 may be performed by a mobile apparatus such as the apparatus 130 described with respect to FIG. 1. The process 2500 may begin after an image capture capability or an application is initiated on the mobile apparatus. In some aspects of the process, the application may enable the image capture feature on the mobile apparatus.

As shown, the process 2500 captures (at 2510) an optical image that includes a vehicle license plate image. As noted above, some aspects of the apparatus may process a video. A frame may then be extracted and converted to an image file.

At 2520, the process 2500 converts the optical image into an electrical signal. The process 2500 then processes (at 2530) the electrical signal to recover license plate information. The process 2500 determines (at 2540) whether the license plate location information was successfully recovered. When the license plate information was successfully recovered, the process proceeds to step 2560 and applies a blocking/replacement process to the location of the license plate image. The process may simply obscure the license plate image with a blocking cover or some other obscuring effect. In another embodiment, the system may replace the license plate image with a preferred image, such as the brand name of an advertiser and the like. When the license plate information was not successfully recovered, the process returns to step 2510 to continue capturing images.

In some instances, a user may wish to shoot video of a vehicle for publishing purposes. FIGS. 26A and 26B conceptually illustrate an exemplary embodiment of a process 2600 of obtaining license plate information from a video and then blocking or replacing the image of the license plate in the video. The process 2600 may be performed by a mobile apparatus such as the apparatus 130 described with respect to FIG. 1. The process 2600 may begin after an image and/or video capture capability or an application is initiated on the mobile apparatus. The application may enable the image and/or video capture feature on the mobile apparatus.

As shown, the process 2600 converts (at 2605) an optical image into an electrical signal for sampling the electrical signal at n frames/second (fps). In some aspects of the process, the process may sample the electrical signal at intervals such as 24 fps or any other suitable interval for capturing video according to the apparatus' capabilities. Each sample of the electrical signal represents a frame of a video image presented on a display. The process 2600 samples (at 2610) a first portion of the electrical signal representing a first frame of the video image presented on the display. The process then determines (at 2615) whether any object image(s) are detected within the frame. At least one of the detected object image(s) may comprise a license plate image. When the process 2600 determines that at least object image exists within the frame, the process 2600 assigns (at 2620) a score based on the detected object image. The score may be based on the likelihood that at least one of the object images is a license plate image and is discussed in greater detail with respect to FIGS. 21 and 22. The score may be applied to each object image and/or aggregated for each object image detected in the frame. The process may then store (at 2625) the score and associated object image and frame information in a data structure. In some aspects of the process, the process 2600 may store the aggregated object image score and/or the process 2600 may store the highest scoring object image in the frame.

When the process 2600 determines (at 2615) that no object image exists within the frame or after the process 2600 stores the score (at 2625), the process 2600 displays feedback to a user based on the object image detected (or not detected). For instance, when no object image is detected in the frame, the process 2600 may display a message guiding the user on how to collect a better optical image. However, when at least one object image is detected in the frame, the process 2600 may provide feedback by overlaying rectangles around the detected object image(s). Alternatively or conjunctively, the process 2600 may overlay a rectangle that provides a visual cue such as a distinct color, indicating which object image is determined to most likely be a license plate image or has a higher score than other object images within the frame. In some aspects, the visual cue may be provided when a particular object image receives a score above a threshold value.

The process 2600 optionally determines (at 2635) whether user input has been received to stop the video. Such user input may include a gestural interaction with the mobile apparatus, which deactivates the camera shutter on the mobile apparatus. When the process 2600 determines (at 2635) that user input to stop the video capture is received, the process 2600 selects (at 2645) the highest scoring frame according to the stored frame information. When the process 2600 determines (at 2635) that user input to stop the video capture has not been received, the process 2600 determines (at 2640) whether to sample additional portions of the electrical signal. In some aspects of the process, such a determination may be based on a predetermined number of samples. For instance, the mobile apparatus may have a built in and/or configurable setting for the number of samples to process before a best frame is selected. In other aspects of the process, such a determination may be based on achieving a score for a frame or object image in a frame that is above a predetermined threshold value. In such aspects, the frame or frame comprising the object image that is above the threshold score will be selected (at 2645). When process 2600 determines that there are more portions of the electrical signal to be sampled, the process 2600 samples (at 2650) the next portion of the electrical signal representing the next frame of the video image presented on the display. The process 2600 then returns to detect (at 2615) object image(s) within the next frame. In some aspects of the process, the process may receive user input to stop the video capture at any point while process 2600 is running. Specifically, the process is not confined to receiving user input to halt video capture after the feedback is displayed (at 2630); the user input may be received at any time while the process 2600 is running. In such aspects, if at least one object image has been scored, then the process 2600 will still select (at 2645) the highest scoring object image. However, if no object images were scored, then the process will simply end.

In some aspects of the process, the process 2600 may optionally use the object image(s) detected in the previous sample to estimate the locations of the object images in the sample. Using this approach optimizes processing time when the process can determine that the mobile apparatus is relatively stable. For instance, the mobile apparatus may concurrently store gyro accelerometer data. The process 2600 may then use gyro accelerometer data retrieved from the mobile apparatus to determine whether the mobile apparatus has remained stable and there is a greater likelihood that the object image(s) will be in similar locations. Thus, when the process 2600 can determine that the mobile apparatus is relatively stable, the processing time for license plate detection may be increased because less of the portion of the electrical signal that represents the video image would need to be searched for the license plate image.

Alternatively or conjunctively, the process 2600 may not use information about object image(s) from the previous frame as a predictor. Instead, the process 2600 may undergo the same detection and scoring process discussed above. Then, for each object image that overlaps an object image detected in a previous frame (e.g., the object images share similar pixels either by space and/or location in the frames), the previous frame receives a higher score. Information about the overlapping object image(s) may be maintained for optimized processing later on, particularly for an embodiment in which the license frame is blocked or replaced in the video image. Additionally, in some aspects of the apparatus, the license plate detection apparatus may maintain a table of matching object image(s) for the sampled portions of the electrical signal representing frames of video images over time. In such aspects, some object image(s) may exist in one or a few of the frames or some may exist in many or all frames and accordingly with higher scores. In such instances, all of the overlapping object images may be processed as discussed in greater detail in the foregoing sections and provided to the server for OCR or identification. This would lead to greater accuracy in actual license plate detection and OCR results.

Returning now to FIGS. 26A and 26B, after selecting (at 2645) the highest scoring frame, the process 2600 processes (at 2655) the electrical signal based on the information associated with the selected frame to recover license plate information. The process 2600 then determines (at 2660) whether license plate information was recovered from the electrical signal. When the process 2600 determines (at 2660) that license plate information has been recovered, the process proceeds to apply the blocking/replacement process (2660) to every frame that includes the license image. When the process 2600 determines (at 2660) that license plate information was not detected, the process returns to step 2640.

In one embodiment the framing step at step 2630 may instead block or replace the possible license image. This may be over-inclusive in blocking the license plate area and other areas, but may otherwise be an acceptable solution for the operation of the system.

The embodiment of the system used in plate blocking/replacement may operate using, for example, the system described in FIG. 6. In this embodiment, the Rendering Module 650 is implemented as illustrated in FIG. 27.

FIG. 27 illustrates an exemplary embodiment of a diagram of the rendering module 650 used with the plate blocking/replacement system. The rendering module 650 may receive as input alert information from the image filter 2735, or information about detected object images from the license plate detector 630. The rendering module will then communicate rendering instructions 2740 to the display 655. The rendering module 650 includes an overlay processor 2705, a Plate Block/Replacement Module 2745, a detection failure engine 2710, and an image renderer 2715.

The overlay processor 2705 receives information about the detected object images 2730 from the license plate detector 630. As discussed above, such information may include coordinates of detected object images and rectangles determined to fit around those object images. The rectangle information is then provided to Plate Block/Replacement module 2745. The module 2745 takes the rectangle information and uses it as a boundary within which to add obscuring data and/or replacement information. Based on the rectangle information, the Plate Block/Replacement module will resize the obscuring information to fit within the rectangle boundary.

The Plate Block/Replacement module 2745 provides it data to the detection failure engine 2710, which will determine that object images have been detected by the license plate detector 630. The detection failure engine 2710 may then forward the information about the rectangles and blocking/replacement image to the image renderer 2715, which will provide rendering instructions 2740 to the display for how and where to display the blocking and/or replacement around the image received from the imager 610. Such information my include pixel coordinates associated with the size and location of the blocking and replacement information and color information. For instance, if the license plate detector 630 determines that a detected object image is more likely to be an actual license plate image than the other detected object images, the rendering module 650 may instruct the display 655 to block or replace that image accordingly.

However, in some instances, the license plate detector 630 may not detect any object images. In such instances, the overlay processor and the module 2745 will not forward any rectangle information to the detection failure engine 2710. The detection failure engine 2710 will then determine there has been an object image detection failure and signal an alert to the image renderer 2715. The image renderer 2715 will then communicate the display rendering instructions 2740 for the alert to the display 655.

Additionally, the image filter 625 may provide information to the image renderer 2715 indicating an alert that the captured image cannot be processed for some reason such as darkness, noise, blur, or any other reason that may cause the image to be otherwise unreadable. The alert information from the image filter 625 is provided to the image renderer 2715, which then provides the rendering display instructions 2740 to the display 655 indicating how the alert will be displayed. The image filter alerts have been discussed in detail above.

FIG. 28 is a flow diagram illustrating the operation of the Blocking/Replacement module 2745. At step 2801 the module receives an image frame and border information. At decision block 2802 the module determines if the border indicates a plate. If not, the system returns to step 2801. If so, the system proceeds to step 2803 to check the user preference for blocking or replacing the plate

At decision block 2804 the system determines if the user has indicated a preference to replace the plate with another image. If so, the system proceeds to step 2806 and selects the replacement image chosen by the user. If not, the system proceeds to decision block 2805 to determine if the user has indicated a preference to obscure the plate information. If not, the system returns to step 2801 and waits for the next image frame.

If the user has indicated a preference for obscuring the plate at decision block 2805, the system proceeds to step 2807 and retrieves the obscuring image selected by the user. The obscuring image could be a solid color, a blurring effect, or some other suitable obscuring image. After step 2807 or after stop 2806, the system proceeds to step 2808 and the image is resized to fit within the borders of the plate in the image frame.

At step 2809 the image frame buffer is updated with the new image in the plate border region as desired. At step 2810 the system can display the image on the user device or in the recording of the image for later reproduction or playback. The system then returns to step 2801 to process the next image frame.

The plate blocking/replacement technique described herein has equal application in cases where image of the plate is skewed and the techniques of FIGS. 19 and 20 are applied. The system has equal application where the plate is rotated, keystoned, or skewed. In one embodiment, an un-warped rectangular block or replacement image is warped to a quadrilateral and then used to block the warped, skewed, or keystoned plate image. In other words, the image can be corrected to be rectangular and blocked or replaced with a rectangular image, or, when the plate is skewed, the blocking or replacement image is reshaped to appropriately block the plate image.

In one embodiment, the system may perform matching of brightness, contrast, blur, color temperature, and the like to make the image appear more natural. In other words, to decrease the perceived discontinuity between the vehicle and the blocking or replacement image. In the case where the replacement image is an advertisement or promotion, the system may render the replacement image for maximum readability, regardless of the associated characteristics of the vehicle image, particularly when the original plate image is of low intensity or contrast.

Alternate Vehicle Identification System Using Redundant Data

One problem in identifying vehicle information from imaging a license plate is the presence of multiple issuing agencies. In general, it is know that each state issues its own license plates. Normally, using location information (e.g., GPS information) available on the mobile device, for example, the system could be biased to identify the license plate of the state in which the image is being taken and or the mobile device is located. However, vehicles from multiple states are typically present in any one location of interest, such that making an assumption that the vehicle belongs to the state in which the image is captured is not always an accurate or correct assumption.

In addition, a license plate identification may be made that identifies an active plate number and associated VIN, but is not in fact the plate being imaged. By using redundant data, such a misidentification may be avoided.

The present system uses a multiphase identification system to improve the accuracy of the system. In one embodiment, when the user invokes the system and prepares to capture an image of the rear of the vehicle and the associated license plate, the system begins to identify vehicle make and model information. In some cases, the name of the vehicle model is present and system can identify the model name (and correspondingly, the make of the vehicle as well). In addition, the system may identify the color of the vehicle during this focusing phase. Thus, in the exemplary aspect, the make, model, and color information may then be used as additional redundant data to double check and/or confirm that the information obtained from the plate reading system is correct.

When the license plate is identified in the second phase of the system, a tentative identification is determined. The license plate information is used to retrieve a VIN associated with the license plate. The VIN includes vehicle make, model, and color information. Based on the make, model, and/or color information (which can also be captured from the vehicle image obtained from the mobile device, for example) matches those identified in the VIN, the likelihood of a correct license plate identification is increased. In this aspect, if the system identified a mismatching of at least one or all of the make, model, and color information, this mismatch likely indicates a misidentification of the vehicle license plate. Moreover, the presence of matching data does not necessarily guarantee a match, but it does increase the likely accuracy of the license plate identification and in some aspects to an acceptable matching threshold (e.g., 99% correctness of a match).

Moreover, in an exemplary aspect, a misidentification of the license plate based on make, model, and/or color may indicate that the wrong issuing state has been identified. In such case, reviewing other states that might have the same license plate number may lead to a positive identification.

In some cases, the vehicle model is not indicated on the portion of the vehicle being imaged during the first phase of the image capture operation. In that case, the system can use other cues to indicate make and model, such as the size, shape, and placement of the taillights of the vehicle. In other cases, the shape and size of other features on the rear of the vehicle may be used as a method to identify the make and model of the vehicle. It is reiterated that each of these physical features of the vehicle can be obtained by a captured image from the mobile device that is uploaded to the system. In one aspect, if certain physical feature information is missing an needed, the system can prompt the prospective seller to obtain one or more additional angles of the vehicle, including providing the user with specific angles (e.g., “direct facing image from rear of vehicle”), for example.

Moreover, it is known that the vehicle year or year range can be determined from styling cues in the vehicle image, as certain features and styles, of taillights, for example, may only be found on certain vehicle years. This provides additional data that can be used to double check the license plate identification information. For example, if the system is able to identify a partial match (e.g., every character of the license plate is identified except one character cannot be recognized as either an “8” or a “B”, for example), and thus identify two or a few vehicles that partially match the license plate, the system can confirm the specific vehicle by using this secondary information (e.g., the physical feature)

In one embodiment, the license plate itself may have identifying characteristics other than the alpha-numeric characters that can identify the issuing state of the vehicle license plate. The state information can be another source of data that can be used to confirm the positive identification of the license plate.

FIG. 29 is a flow diagram illustrating the operation of the system in one embodiment. At step 2901 the image capture process is initiated, using the algorithms as described above. For example, in one embodiment this is accomplished by a user with a mobile phone, for example. The system then initiates a plurality of processes, including plate detector 2902, car detector 2911, logo detector 2914, and/or tail light and characteristic detector 2917. Each of these processes identifies certain information about the vehicle, and the results of the processes can be used as an additional verification of the plate identification, increasing the confidence score of a perceived match, as described above.

The plate detector process 2902 proceeds to the dewarp process 2903 that may operate as described in FIG. 19 above. Next, the process further proceeds to state classifier 2904 where, based on information on the plate, the state of the plate issuer can be determined to increase the confidence score of a match. The state is identified at step 2905. After the dewarp process the system proceeds to character detection and segmentation step 2906. The characters that are detected are OCR'd at step 2907 (such as using the OCR module 660) and the system proceeds to syntax step 2908

At step 2908, the system may be configured to recognize particular patterns that various states use for standard issued vehicle license plates. For example, the state of Colorado generally has two very common patterns, both generally containing six characters with a symbol representative of an icon, sticker, picture, or the like, that is not a number or letter. The two common text patterns may be ###-XXX and XXX-###, where “#” means a number between 0 and 9, “X” means a letter between A and Z, and the dash symbol “-” means any icon, sticker, picture, and the like, that is not a number or letter. According to an example embodiment, when the system identifies a license plate that is at least 99% certain that the identified license plate is the state of Colorado and the text is “0GH-B52”, the system is also configured to determine that the license plate has been incorrectly identified. That is, while the system may determine that the dash symbol ensures that the license plate is not a mere vanity plate, the system is configured to determined that a Colorado license plate must fit one of patterns ###-XXX and XXX-###. In the example, the text “0GH-B52” fits a pattern #XX-X## that is not recognized by the system to be identified as a Colorado license plate. As such, the system may determine if certain characters in a text “0GH-B52” are misread. For example, in the text “0GH-B52” the first character may be the letter Q, O, or D instead of the number 0, and the letter B may have been a misread number 8. Alternatively, the system may be configured to determine that the identified license plate is not the state of Colorado and check to determine if there are other state license plates that fit the pattern #XX-X## and are commonly misclassified as a Colorado license plate. As noted above, upon detecting a mismatch, the user can be prompted to provide an additional image of the vehicle's license plate.

Moreover, it is noted that while the above example embodiment considers patterns consisting of numbers, letters, and dashes, the present invention is not limited thereto and may consider actual character patterns in an alternate embodiment. For example, the letter “Q” are likely to occur is some letter position in a Colorado license plate and less likely in other states. Thus, in the example text “0GH-B52” it may be more likely that the number “0” is actually a letter “Q”. Whereas in a California state license plate, where the letter “Q” is extremely rare, if the system identifies the letter “Q” in a California license plate, the system syntax for a California state license plate would indicate that the system identified the wrong license plate. The system can use these different probabilities to limit the possible variations of the license plate and then compare possible matches with detected physical features of the vehicle to confirm a possible identification of the vehicle.

Next, at step 2909, the character string is provided to the System API to use to identify the vehicle. The car detector at step 2911 is provided to use physical features associated with the vehicle to provide independent and redundant corroboration of the vehicle identification. As described above, this may be by analyzing the shape of the rear of the car, by detecting the name of the manufacturer, for example, or other identifying characteristics, including the name of the model that may appear on the rear of the vehicle near the license plate region. The information is provided to car classifier step 2912 and at step 2913 the make, model, and year range may be identified. It should be noted that while certain physical features are described as providing the independent and redundant corroboration of the vehicle identification, the entire image of the vehicle can be used to corroborate vehicle identification according to an alternative aspect. In other words, the year, make and model of the vehicle can be corroborated using detection algorithms based on an entire image of the vehicle using the same or similar algorithms as described above.

In any event, this collected information is then provided to the API 2909. One embodiment of the system uses logo detector 2914 to detect a manufacturer logo on the rear of the vehicle. This may be the Chevrolet bow tie, for example, the Ford oval, the Mercedes star and the like. The information is forwarded to step 2915, the logo classifier, to determine the make at step 2915.

As an additional method of identifying the vehicle, the system may use tail light and characteristic detector step 2917. Many vehicles have distinctive tail lights that indicate both the make and model of the vehicle, but can also indicate a model year or model year range. The system includes a database of taillight information and classifier step 2918 is used to attempt to use the information to provide make, model, edition, and year range at step 2919.

The system API 2909 uses all of the information to determine the vehicle information. If the license plate indicates registration of a Mercedes, but the car detector (and/or logo detector or taillight characteristic detector) indicates that the vehicle is an Audi. The system would flag this and let the user know so that some action may be taken.

In one embodiment, the system does not utilize or engage the redundant data unless the confidence score of the plate reading operation is below a certain threshold (e.g., below 90% confidence). FIG. 30 is a flow diagram illustrating the operation of the redundant data embodiment using the threshold system. At step 3001 the system reads the car plate using the algorithms and techniques described above. At step 3001, it is determined if the confidence is below a desired threshold. If so, the system proceeds to step 3003 and engages the car detector operations. At step 3004, the car make and model data is determined and the system returns to step 3002. If the confidence is not below the threshold at step 3002, the vehicle is considered identified as a match at step 3005.

In one embodiment, the car detector steps are performed on the mobile device using a local database of logos, taillights, and other identifying information. In another embodiment, the plate identification process is performed in the cloud. In one embodiment, both processes are performed in the cloud.

As generally shown, FIG. 23 illustrates an exemplary a system 2300 that may implement the license plate detection apparatus. The electronic system 2300 of some embodiments may be a mobile apparatus. The electronic system includes various types of machine readable media and interfaces. The electronic system includes a bus 2305, processor(s) 2310, read only memory (ROM) 2315, input device(s) 2320, random access memory (RAM) 2325, output device(s) 2330, a network component 2335, and a permanent storage device 2340.

The bus 2305 communicatively connects the internal devices and/or components of the electronic system. For instance, the bus 2305 communicatively connects the processor(s) 2310 with the ROM 2315, the RAM 2325, and the permanent storage 2340. The processor(s) 2310 retrieve instructions from the memory units to execute processes of the invention.

The processor(s) 2310 may be implemented with one or more general-purpose and/or special-purpose processors. Examples include microprocessors, microcontrollers, DSP processors, and other circuitry that can execute software. Alternatively, or in addition to the one or more general-purpose and/or special-purpose processors, the processor may be implemented with dedicated hardware such as, by way of example, one or more FPGAs (Field Programmable Gate Array), PLDs (Programmable Logic Device), controllers, state machines, gated logic, discrete hardware components, or any other suitable circuitry, or any combination of circuits.

Many of the above-described features and applications are implemented as software processes of a computer programming product. The processes are specified as a set of instructions recorded on a machine readable storage medium (also referred to as machine readable medium). When these instructions are executed by one or more of the processor(s) 2310, they cause the processor(s) 2310 to perform the actions indicated in the instructions.

Furthermore, software shall be construed broadly to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. The software may be stored or transmitted over as one or more instructions or code on a machine-readable medium. Machine-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium may be any available medium that can be accessed by the processor(s) 2310. By way of example, and not limitation, such machine-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a processor. Also, any connection is properly termed a machine-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared (IR), radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Thus, in some aspects machine-readable media may comprise non-transitory machine-readable media (e.g., tangible media). In addition, for other aspects machine-readable media may comprise transitory machine-readable media (e.g., a signal). Combinations of the above should also be included within the scope of machine-readable media.

Also, in some embodiments, multiple software inventions can be implemented as sub-parts of a larger program while remaining distinct software inventions. In some embodiments, multiple software inventions can also be implemented as separate programs. Any combination of separate programs that together implement a software invention described here is within the scope of the invention. In some embodiments, the software programs, when installed to operate on one or more electronic systems 2300, define one or more specific machine implementations that execute and perform the operations of the software programs.

The ROM 2315 stores static instructions needed by the processor(s) 2310 and other components of the electronic system. The ROM may store the instructions necessary for the processor(s) 2310 to execute the processes provided by the license plate detection apparatus. The permanent storage 2340 is a non-volatile memory that stores instructions and data when the electronic system 2300 is on or off. The permanent storage 2340 is a read/write memory device, such as a hard disk or a flash drive. Storage media may be any available media that can be accessed by a computer. By way of example, the ROM could also be EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

The RAM 2325 is a volatile read/write memory. The RAM 2325 stores instructions needed by the processor(s) 2310 at runtime, the RAM 2325 may also store the real-time video images acquired during the license plate detection process. The bus 2305 also connects input and output devices 2320 and 2330. The input devices enable the user to communicate information and select commands to the electronic system. The input devices 2320 may be a keypad, image capture apparatus, or a touch screen display capable of receiving touch interactions. The output device(s) 2330 display images generated by the electronic system. The output devices may include printers or display devices such as monitors.

The bus 2305 also couples the electronic system to a network 2335. The electronic system may be part of a local area network (LAN), a wide area network (WAN), the Internet, or an Intranet by using a network interface. The electronic system may also be a mobile apparatus that is connected to a mobile data network supplied by a wireless carrier. Such networks may include 3G, HSPA, EVDO, and/or LTE.

It is understood that the specific order or hierarchy of steps in the processes disclosed is an illustration of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged. Further, some steps may be combined or omitted. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.

The various aspects of this disclosure are provided to enable one of ordinary skill in the art to practice the present invention. Various modifications to exemplary embodiments presented throughout this disclosure will be readily apparent to those skilled in the art, and the concepts disclosed herein may be extended to other apparatuses, devices, or processes. Thus, the claims are not intended to be limited to the various aspects of this disclosure, but are to be accorded the full scope consistent with the language of the claims. All structural and functional equivalents to the various components of the exemplary embodiments described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. § 112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.” 

What is claimed is:
 1. A mobile apparatus, comprising: an image sensor configured to convert at least one optical image into an electrical signal, the at least one optical image including an image of a vehicle license plate; and a license plate detector configured to: identify, by a processor, one or more object images from the at least one optical image based on the electrical signal, with each of said one or more object images comprising a candidate vehicle license plate image, crop, by the processor, the at least one optical image to said identified one or more of the object images, score, by the processor, each of said cropped one or more of the object images based on a probability that the cropped object image comprises the vehicle license plate image, based on said scored object images, obtain vehicle information generated from the candidate vehicle license plate image, the obtained vehicle information including at least one physical feature of a possible vehicle, detect vehicle characteristic information from the at least one optical image, and compare the detected vehicle characteristic information to the obtained vehicle information generated from the candidate vehicle license plate image to determine an estimated level of accuracy for the candidate vehicle license plate image.
 2. The mobile apparatus of claim 1, wherein the vehicle characteristic information comprises at least one of a make, model, year, and color of a vehicle in the at least one optical image.
 3. The mobile apparatus of claim 1, wherein the vehicle characteristic information comprises at least one of size, shape, and taillight placement of a vehicle in the at least one optical image.
 4. The mobile apparatus of claim 1, wherein the vehicle license plate includes identifying characteristics on the vehicle license plate itself to identify an issuing state of the vehicle license plate.
 5. The mobile apparatus of claim 1, further comprising a logo detector configured to detect a vehicle manufacture logo of a vehicle in the at least one optical image.
 6. The mobile apparatus of claim 1, wherein the license plate detector is further configured to recover the information from a portion of the electrical signal corresponding to the selected one of said identified one or more of the object images.
 7. The mobile apparatus of claim 1, wherein the license plate detector is further configured to process the electrical signal to dewarp at least one of said one or more of the object images.
 8. The mobile apparatus of claim 1, further comprising a display and a rendering module configured to render the at least one optical image to the display and overlay a detection indicator on each of said identified one or more of the object images in the at least one optical image.
 9. The mobile apparatus of claim 8, wherein each of the cropped one or more object images is scored by: detecting a plurality of alphanumeric characters in the object image, and applying a score based on a position of one of the plurality of characters relative to a position of another one of the plurality of characters, and
 10. The mobile apparatus of claim 9, wherein the license plate detector is further configured to process the electrical signal to score each of the plurality of object images based on a probability that the each of the plurality of object images comprises the vehicle license plate image.
 11. The mobile apparatus of claim 10, wherein the license plate detector is further configured to select said one of the plurality of object images based on the selected object image's score.
 12. The mobile apparatus of claim 12, wherein the rendering module is further configured to overlay a first detection indicator over said one of the plurality of object images and a second detection indicator over the other object images of said plurality of object images.
 13. The mobile apparatus of claim 12, wherein the rendering module is further configured to cause the first detection indicator to appear visually different from the second detection indicator on the display.
 14. The mobile apparatus of claim 8, further comprising an image filter configured to apply one or more filter parameters to the electrical signal based on at least one of color temperature of the image, ambient light, and motion of the mobile apparatus.
 15. The mobile apparatus of claim 14, wherein the rendering module is further configured to overlay a detection indicator on the displayed optical image to assist the user in positioning the apparatus with respect to the optical image in response to a signal from the image filter, and wherein the rendering module is further configured to provide an alert to the display when the license plate detector fails to recover the vehicle license plate information if the estimated level of accuracy for the candidate vehicle license plate image is below a predefined threshold.
 16. A method for detecting a vehicle, the method comprising: converting, by an image sensor, at least one optical image into an electrical signal, the at least one optical image including an image of a vehicle license plate; identifying, by a processor, one or more object images from the at least one optical image based on the electrical signal, with each of said one or more object images comprising a candidate vehicle license plate image; cropping, by the processor, the at least one optical image to said identified one or more of the object images; scoring, by the processor, each of said cropped one or more of the object images based on a probability that the cropped object image comprises the vehicle license plate image; based on said scored object images, obtaining, by the processor vehicle information generated from the candidate vehicle license plate image, the obtained vehicle information including at least one physical feature of a possible vehicle; detecting, by the processor, vehicle characteristic information from the at least one optical image; and comparing, by the processor, the detected vehicle characteristic information to the obtained vehicle information generated from the candidate vehicle license plate image to determine an estimated level of accuracy for the candidate vehicle license plate image.
 17. The method of claim 16, wherein the vehicle characteristic information comprises at least one of a make, model, year, and color of a vehicle in the at least one optical image.
 18. The method of claim 16, wherein the vehicle characteristic information comprises at least one of size, shape, and taillight placement of a vehicle in the at least one optical image.
 19. The method of claim 16, wherein the vehicle license plate includes identifying characteristics on the vehicle license plate itself to identify an issuing state of the vehicle license plate.
 20. The method of claim 16, further comprising detecting, by the processor, a vehicle manufacture logo of a vehicle in the at least one optical image.
 21. The method of claim 16, further comprising recovering, by the processor, the information from a portion of the electrical signal corresponding to the selected one of said identified one or more of the object images.
 22. The method of claim 16, further comprising processing the electrical signal to dewarp at least one of said one or more of the object images.
 23. The method of claim 16, further comprising: rendering the at least one optical image on a display; and overlaying a detection indicator on each of said identified one or more of the object images in the at least one optical image.
 24. The method of claim 23, further comprising scoring each of the cropped one or more object images by: detecting a plurality of alphanumeric characters in the object image, and applying a score based on a position of one of the plurality of characters relative to a position of another one of the plurality of characters, and
 25. The method of claim 24, further comprising scoring, by the processor, each of the plurality of object images based on a probability that the each of the plurality of object images comprises the vehicle license plate image.
 26. The method of claim 25, further comprising selecting said one of the plurality of object images based on the selected object image's score.
 27. The method of claim 26, further comprising overlaying a first detection indicator over said one of the plurality of object images and a second detection indicator over the other object images of said plurality of object images.
 28. The method of claim 27, further comprising causing the first detection indicator to appear visually different from the second detection indicator on the display.
 29. The method of claim 16, further comprising applying, by an image filter, one or more filter parameters to the electrical signal based on at least one of color temperature of the image, ambient light, and motion of the mobile apparatus.
 30. The method of claim 29, further comprising: overlaying a detection indicator on the displayed optical image to assist the user in positioning the apparatus with respect to the optical image in response to a signal from the image filter; and providing an alert to the display when the license plate detector fails to recover the vehicle license plate information if the estimated level of accuracy for the candidate vehicle license plate image is below a predefined threshold. 